Human Trust API Data Confidence Fabric
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Solution Overview
Problem
Existing systems face challenges in determining the trustworthiness of human users, as evaluating reliability can be difficult, especially when data sources are unclear or unreliable, making it hard to assign confidence levels effectively.
Innovation Solution
The use of data confidence fabric technology, combining human interaction data from various devices like cameras, mobile devices, and AI, to generate and share a trustworthiness score for human users, allowing them to create a 'trust brand' and grant access to devices for data collection, while maintaining control over measurements and data within the fabric.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data confidence fabric technology is used to generate trustworthiness scores from human interaction data, then the reliability of evaluating human user trustworthiness is improved, but the device complexity increases due to integration of multiple devices and systems
Solution Approach 1:
The system segments the trust evaluation process into separate modules: data collection from multiple devices, data analysis, trustworthiness score generation, and score sharing. Each module operates independently, reducing overall system complexity while maintaining comprehensive evaluation capability across cameras, mobile devices, and AI systems
Solution Approach 2:
The data confidence fabric acts as an intermediary layer between multiple data sources (cameras, mobile devices, AI systems) and the trust evaluation process. This mediator consolidates data from diverse sources into a unified trustworthiness assessment, simplifying the integration complexity while improving evaluation reliability
2Measurement precision
If comprehensive data collection from multiple devices is implemented, then the measurement precision of trustworthiness evaluation is improved, but the loss of information increases due to data privacy concerns and control requirements
Solution Approach 1:
The system enables users to maintain control over their own data and trustworthiness scores through self-service mechanisms. Users can authorize data collection from specific devices, control which scores are shared externally, and manage their privacy settings, thereby preventing information loss while maintaining comprehensive measurement
Solution Approach 2:
The system applies local quality control by allowing different data collection and sharing policies for different contexts and devices. Users can selectively enable or disable data collection from specific sources based on their privacy preferences, maintaining measurement precision for trusted sources while protecting sensitive information
3Productivity
If trustworthiness scores are shared across ecosystems, then the productivity of business transactions is improved, but the reliability of trust evaluation decreases due to varying data source quality across different systems
Solution Approach 1:
The system dynamically adjusts trust evaluation parameters based on the quality and reliability of data sources from different ecosystems. When data from a trusted source is available, the system uses it to enhance the trustworthiness score; when data quality is uncertain, the system applies different weighting or requires additional verification, thereby maintaining reliability while enabling cross-ecosystem sharing
Solution Approach 2:
The trust evaluation system operates dynamically, adapting its methodology based on the specific data sources involved in each transaction. The system can switch between different evaluation algorithms and weightings depending on the ecosystem and data quality, allowing efficient cross-ecosystem productivity while maintaining reliable trust evaluation through context-aware adjustments
Data Source
AI summary
One example method includes receiving authorization from a human user to collect data concerning an interaction of the human user with a computing element, interacting with the human user, collecting data concerning the interaction, analyzing the collected data, generating trust and confidence information, concerning the human user, based on analysis of the collected data, and storing the trust and confidence information.